\\\\Introduction\\\\ We are seeking a highly skilled and experienced Senior Data Scientist with deep expertise in Machine Learning, Deep Learning, and Generative AI. The ideal candidate will have a strong track record of delivering end-to-end data science solutions in production environments and hands-on experience with LLMs and Agentic AI frameworks. You will be responsible for driving AI/ML projects from ideation to deployment, collaborating with cross-functional teams, and ensuring scalable implementations. \\\\Your role and responsibilities\\\\ \\\ Execute end-to-end Data Science projects including data collection, preprocessing, feature engineering, modelling, evaluation, and deployment. \\\ Design and implement advanced Machine Learning algorithms (classification, regression, clustering, ensemble methods) and Natural Language Processing algorithms (Knowledge Graphs, Topic Modelling, Feature Extraction, Sentiment Analysis, BERT etc.) \\\ Develop and deploy Generative AI and LLM-based solutions using platforms like OpenAI, Hugging Face, and LLama. \\\ Apply Agentic AI frameworks such as LangChain, LangGraph, Crew AI, or Microsoft Semantic Kernel to build intelligent applications. \\\ Proficient in designing and deploying scalable machine learning solutions using cloud architectures, with hands-on experience in at least one major platform (Azure, AWS, GCP, or IBM Cloud). Skilled in leveraging Databricks, ML platforms, managed databases, web hosting services, and document AI tools for end-to-end solution development. \\\ Collaborate with business stakeholders to define problem statements, deliver insights, and drive impact. \\\ Maintain reproducibility and version control using Git, GitHub \\\ Effectively communicate technical concepts and project outcomes to both technical and non-technical stakeholders. \\\\Required technical and professional expertise\\\\ \\\ 7-9 years of hands-on experience in Data Science, Machine Learning, Deep Learning, and NLP in a production environment. \\\ 2+ years of applied experience in Generative AI and LLMs (e.g., OpenAI, LLama). \\\ Proficiency in agentic AI development using frameworks like LangChain, LangGraph, or similar. \\\ Strong programming skills in Python and experience with ML/DL libraries \\\ Experience deploying models via REST APIs or web applications. \\\ Proficient in SQL for data extraction, transformation, and analysis. \\\ Experience working with large datasets, feature engineering, and data preprocessing pipelines. \\\ Solid understanding of model evaluation, cross-validation, and performance metrics. \\\ Experience in MLOps, including model testing, deployment pipelines, governance frameworks, and continuous monitoring for reliable and compliant machine learning operations. \\\ Experience with cloud ML pipelines and services (proficiency in at least one of Azure, AWS, GCP & IBM Cloud). \\\* Strong interpersonal and client communication skills. IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

The role requires 7-9 years of hands-on experience in Data Science, Machine Learning, Deep Learning, and NLP within a production environment. A minimum of 2 years of applied experience in Generative AI and LLMs, such as OpenAI or LLama, is necessary. Proficiency in agentic AI development using frameworks like LangChain or LangGraph is also a requirement. Strong programming skills in Python and experience with ML/DL libraries are essential, along with the ability to deploy models via REST APIs or web applications. The position also demands proficiency in SQL for data extraction and analysis, experience with large datasets and preprocessing, and a solid understanding of model evaluation. Experience in MLOps, including deployment pipelines and continuous monitoring, is required, as is experience with cloud ML pipelines and services on at least one major platform (Azure, AWS, GCP, or IBM Cloud).